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Enregistrement W3185073343 · doi:10.1002/mdc3.13313

Expiratory Muscle Strength Training in Patients with Parkinson's Disease: A Pilot Study of Mobile Monitoring Application

2021· article· en· W3185073343 sur OpenAlex

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Notice bibliographique

RevueMovement Disorders Clinical Practice · 2021
Typearticle
Langueen
DomaineHealth Professions
ThématiqueDysphagia Assessment and Management
Établissements canadiensnon disponible
Organismes subventionnairesAgentura Pro Zdravotnický Výzkum České RepublikyČeské Vysoké Učení Technické v Praze
Mots-clésMedicineAudio feedbackPhysical medicine and rehabilitationPhysical therapyParkinson's diseaseBiofeedbackExhalationDiseaseAnesthesia

Résumé

récupéré en direct d'OpenAlex

Expiratory muscle strength training (EMST) studies have reported significant improvements in maximum expiratory strength, cough efficacy, and swallowing function in patients with Parkinson's disease (PD).1 Currently, EMST is usually performed in short and intensive training periods.1 However, information about detraining outcomes highlights the need for the development of long-term maintenance home-based programs to sustain training gains following intensive periods of EMST, especially considering the progressive nature of PD.2 Nevertheless, low long-term adherence to home exercise is an important issue in many patient groups and may compromise treatment outcomes.3 Therefore, we developed a mobile phone-based visual feedback (MPVF) application to keep patients motivated to continue EMST following intensive periods of training. With the help of a specially developed algorithm, the application can evaluate real-time data using a microphone attached to an expiratory handheld device (Fig. 1). Supplement S1 provides detailed information about the algorithm. The first step in the research process of examining whether the feedback application is effective in long-term EMST adherence is to find out whether a MPVF application is suitable for patients with PD who may experience difficulties in using a smartphone due to motor and cognitive problems related to age and PD itself. Therefore, we conducted a pilot study to investigate the usability of MPVF in EMST in patients with PD. A total of 12 patients (Table S1) with PD performed an intensive home-based EMST with MPVF for 2 weeks. Peak cough flow, maximum expiratory pressure, and maximum inspiratory pressure were measured. The usability of the MPVF was also assessed using a semistructured interview. Supplement S2 provides detailed information about the methods. The median of adherence for completing the prescribed exercises in the training period was 90.5% (between 174 and 431 completed EMST maneuvers). A total of 2 weeks of intensive EMST were sufficient to significantly improve the participants' maximum expiratory pressure and voluntary peak cough flow (Table S2). The improvement is quantitatively comparable with the results of other intensive EMST studies with longer durations, for example.4, 5 When interpreting such rapid improvement, the impact of visual feedback should be considered. It can be assumed that visual feedback increased training effort compared with regular training without immediate control. All participants appreciated EMST with MPVF (12/12). They found it motivating (11/12), comprehensible (11/12), and user friendly (10/12). Even participants (n = 3) with mild cognitive impairment (Montreal Cognitive Assessment scores 24–25) who coincidentally had no previous experience with smartphones were able to use the application without difficulties or help from another person. With respect to the suggestions for improvement from a semistructured interview, the patients might be divided into 2 groups. The first group proposed simplifying the application as much as possible. The second group preferred more advanced options of presenting training data, such as in overview graphs and charts. To meet the needs of both groups, the application should be able to work in basic and advanced modes in the future. These findings indicate that EMST coupled with MPVF is feasible and potentially useful in patients with PD. We thank Tomas Sieger, PhD, from the Czech Technical University in Prague for his advice on the method of instantaneous sound level calculation. We also thank Ota Gal, PhD, from the Department of Neurology and Centre of Clinical Neuroscience, General University Hospital in Prague for manuscript review and critique. (1) Research Project: A. Conception, B. Organization, C. Execution; (2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; (3) Manuscript Preparation: A. Writing of the First Draft, B. Review and Critique. M.S.: 1A, 1B, 1C, 2A, 2B, 2C, 3A R. Korteová: 1A, 1B, 1C, 2C R. Kliment: 1A, 3A R.J.: 3B E.R.: 1A, 2C, 3B M.H.: 1A, 3B This study was approved by the Ethics Committee of the General University Hospital in Prague (No. 1613/19 S-IV). All participants signed an informed consent. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. This work was supported by The EU Joint Programme–Neurodegenerative Disease Research 8F19003iCARE-PD and Ministry of Health of the Czech Republic Grants No NV19-04-00233 and NU20-04-0337. The authors declare that there are no conflicts of interest relevant to this work. The authors declare that there are no additional disclosures to report. Supplement S1 The MPVF algorithm. Supplement S2 The methods. Table S1 Demographic and clinical characteristics. Table S2 Median and interquartile range (IQR) of maximum respiratory pressure and voluntary cough values across pre- and posttesting time points. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,069
Score d'incertitude au seuil0,718

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,073
Tête enseignante GPT0,445
Écart entre enseignants0,372 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle